Traffic state estimation on highway: A comprehensive survey
Traffic state estimation (TSE) refers to the process of the inference of traffic state variables
(ie, flow, density, speed and other equivalent variables) on road segments using partially …
(ie, flow, density, speed and other equivalent variables) on road segments using partially …
Short-term travel-time prediction on highway: a review of the data-driven approach
Near future travel-time information is one of the most critical factors that travellers consider
before making trip decisions. In efforts to provide more reliable future travel-time estimations …
before making trip decisions. In efforts to provide more reliable future travel-time estimations …
T-gcn: A temporal graph convolutional network for traffic prediction
Accurate and real-time traffic forecasting plays an important role in the intelligent traffic
system and is of great significance for urban traffic planning, traffic management, and traffic …
system and is of great significance for urban traffic planning, traffic management, and traffic …
Hierarchical graph convolution network for traffic forecasting
Traffic forecasting is attracting considerable interest due to its widespread application in
intelligent transportation systems. Given the complex and dynamic traffic data, many …
intelligent transportation systems. Given the complex and dynamic traffic data, many …
Optimized graph convolution recurrent neural network for traffic prediction
Traffic prediction is a core problem in the intelligent transportation system and has broad
applications in the transportation management and planning, and the main challenge of this …
applications in the transportation management and planning, and the main challenge of this …
[图书][B] Data assimilation fundamentals: A unified formulation of the state and parameter estimation problem
This open-access textbook's significant contribution is the unified derivation of data-
assimilation techniques from a common fundamental and optimal starting point, namely …
assimilation techniques from a common fundamental and optimal starting point, namely …
Dynamic graph convolution network for traffic forecasting based on latent network of Laplace matrix estimation
Traffic forecasting is a challenging problem in the transportation research field as the
complexity and non-stationary changing of the traffic data, thus the key to the issue is how to …
complexity and non-stationary changing of the traffic data, thus the key to the issue is how to …
[HTML][HTML] Multistep traffic forecasting by dynamic graph convolution: Interpretations of real-time spatial correlations
Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy
decisions in advanced traffic control and guidance systems. Recently, deep learning …
decisions in advanced traffic control and guidance systems. Recently, deep learning …
A noise-immune Kalman filter for short-term traffic flow forecasting
L Cai, Z Zhang, J Yang, Y Yu, T Zhou, J Qin - Physica A: Statistical …, 2019 - Elsevier
This paper formulates the traffic flow forecasting task by introducing a maximum correntropy
deduced Kalman filter. The traditional Kalman filter is based on minimum mean square error …
deduced Kalman filter. The traditional Kalman filter is based on minimum mean square error …
Real-time traffic speed estimation with graph convolutional generative autoencoder
Real-time traffic speed estimation is an essential component of intelligent transportation
system (ITS) technologies. It is the foundation of modern transportation control and …
system (ITS) technologies. It is the foundation of modern transportation control and …